Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to enhance model generalizability

Surgical Management of Lumbar Spine Fractures and Dislocations

Background: Lumbar spine fractures and dislocations, which are part of the thoracolumbar region, are critical injuries with significant morbidity. The epidemiological shift in the median age of injury and the high prevalence of these injuries, particularly in the T10-L2 region, highlight the necessity for effective therapeutic interventions. With advancements in spine biomechanics, imaging technologies, and surgical techniques, there has been a paradigm shift from conservative to surgical management, though high-quality comparative studies remain limited. Objective: To synthesize recent data on the epidemiology, evaluation, and management of lumbar spine fractures and dislocations, and to elucidate the comparative efficacy of surgical interventions and conservative approaches in optimizing patient outcomes. Method: This paper conducts a comprehensive review of epidemiological data on thoracolumbar traumatic injuries, diagnostic techniques, and management strategies, especially focusing on surgical interventions. The review also details specific surgical techniques utilized for lumbar spine fractures and their underlying rationale. Findings and Conclusion: Thoracolumbar injuries primarily affect the transitional zone (T11-L2) and show a higher incidence in males aged between 20 and 40. Imaging, especially CT scans, offers a definitive diagnostic approach, with MRI providing insights on soft tissue interactions. While historically, conservative methods dominated therapeutic interventions, surgical techniques, including Posterior Instrumentation, Anterior Lumbar Interbody Fusion (ALIF), Transforaminal Lumbar Interbody Fusion (TLIF), and Posterior Lumbar Interbody Fusion (PLIF), are increasingly being utilized. Some specific fractures even warrant a combined posterior-anterior surgical approach. Notably, certain case studies highlight the potential for superior outcomes with surgical intervention, even in the absence of neurological deficits. Selecting the appropriate management strategy should be tailored to individual patient factors, nature of the injury, and available expertise and resources

A Collaborative Systems of Systems Simulation of Urban Air Mobility: Architecture Process and Demonstration of Capabilities

Urban Air Mobility (UAM) presents a complex challenge in aviation due to the high degree of innovation required across multiple domains to realize it. From the use of advanced aircraft powered by new technologies, the management of the urban air space to enable high density operations, to the operation of specialized vertidromes serving as a start and end point of the vehicles, the UAM paradigm necessitates a significant departure from aviation as we know it today. In order to understand and assess the many facets of this new paradigm, a Collaborative Agent-Based Simulation is developed to holistically evaluate the system through the modelling of the stakeholders. In this regard, models of vertidrome air-side operations, urban air space management, passenger demand estimation and mode choice, vehicle operator cost and revenues, vehicle maintenance, vehicle allocation, fleet management based on vehicle design performance and mission planning are brought together into a single Collaborative System of Systems Agent-Based Simulation of Urban Air Mobility. Through collaboration, higher fidelity models of each domain can be brought together into a single environment which can then be exploited by all partners, achieving comprehensiveness and fidelity levels not achievable by a single partner. Furthermore, the integration enables the capture of cross-domain effects with ease and allows the domain-specific studies to be evaluated at a holistic level. Agent-Based Simulations were chosen for this collaborative effort as it presents a suitable platform for the modelling of the stakeholders and interactions in accordance with the envisioned concept of operations. This work presents the capabilities of the developed Collaborative System of Systems Agent-based Simulation, the development process and finally a visual demonstration. The objectives of this presentation are: • Detail the development process of the Collaborative System of Systems Agent-Based Simulation • Demonstrate a holistic simulation of UAM built through collaboration of multiple tools/modules such as vertiport and trajectorie

Integrating ATM and air transport into multimodal transport system for Door-to-Door travel: the X-TEAM D2D project proposed approach

The project designed ConOps has been finally validated by the construction of a simulation model of a high-level D2D case study, with the aim of assessing the performances, feasibility and limitations of the ConOps and identifying the areas of improvement between ATM and the different modes of transport. The simulation model included two parts: the regional airport and related addressed surrounding area (namely Brunswick, served by Hannover airport) and the hub airport and relatedaddressed surrounding area (namely Haarlem, served by Schiphol airport). The simulation model maintained the same structure but included different transport options in 2025, 2035 and 2050. The model implemented both the different existing transport options and perspective transfer possibilities compliant with the X-TEAM D2D designed ConOps, using real-life distances (GIS-Based) and reallife travel times (GIS-based) for the existing possibilities. It included moving objects (carrying info), such as passenger groups and transport entities, and static elements (Capacitated nodes), such as transport network and transfer stations

Integrating ATM and air transport into multimodal transport system for Door-to-Door travel: the X-TEAM D2D project proposed approach

The X-TEAM D2D project activities have been successfully concluded in 2022 and delivered several relevant results. First, the project provided the definition of future scenarios and use cases for the integration of the vertical transport with the surface transport towards integrated intermodal transport system and the identification of the resulting barriers. Based on that, the project carried out the design of the ConOps for integration of ATM and aviation, as well as UTM and UAM, in intermodal transport infrastructure and the parallel and cooperative design of the ConOps for the integration of ATM and UTM into overall intermodal service to passengers. The resulting overall ConOps for the seamless integration of ATM and air transport into an overall intermodal network, including other available transportation means (surface, water), to support the door-to-door connectivity has been finally successfully validated by means of dedicated simulation environment setup by the project as well as external experts assessment. The validation results indicated the feasibility and effectiveness of the proposed ConOps in achieving its target as well as allowed providing suggestions for its future development and improvement, beyond the project scope

The Impact of the Application of Artificial Intelligence on Decision-Making "Applied Study on Saudi Government Hospitals in Najran region"

The study aimed to explanation of the impact of the application of artificial intelligence on decision-making at Saudi government hospitals in Najran region. The study adopted the descriptive analytical approach to show and describe artificial intelligence and its impact on decision-making at Saudi government hospitals in Najran region. The study used an electronic questionnaire that was applied to (297) employees at Saudi Government Hospitals in Najran region. The study results showed that there is a high degree of appreciation for artificial intelligence at Saudi government hospitals in Najran region, where this is due to the efficiency of its dimensions. The study showed that the Smart Agents was in the first rank, in the second rank came the Expert Systems, and in the third rank was Genetic Algorithms, where these dimensions were at a high level of appreciation. Moreover, in the fourth and last rank was the Neural Networks, at moderate level of appreciation. These results indicate a high degree of Artificial intelligence at Saudi governmental hospitals in Najran region. The study also found a statistically significant effect at the significance level (0.05 ≥ α) of Artificial Intelligence (Expert Systems, Neural Networks, Genetic Algorithms, Smart Agents) on Decision-Making in Saudi governmental hospitals in Najran region. In light of the results, the study recommended enhancing the use of artificial intelligence in the decision-making process at Saudi hospitals

An Outline of A Concept of Operations fFor Integration of ATM and Air Transport into Multimodal Transport System for Door-fo-Door Travel

In the framework of the research activities supported by SESAR JU, dedicated research stream is devoted to investigation of integration of Air Traffic Management (ATM) and aviation into a wider transport system able to support the implementation of Door-to-Door (D2D) travel concept. In this framework, the project X-TEAM D2D (Extended ATM for Door-to-Door Travel) has been funded by SESAR JU under the call SESAR-ER4-10-2019: ATM Role in Intermodal Transport, with Grant Agreement n. 891061. The project aims defining, developing and initially validating a Concept of Operations (ConOps) for the seamless integration of ATM and air transport into an overall intermodal network, including other available transportation means (surface, water), to support the door-to-door connectivity, in up to 4 hours, between any location in Europe, in compliance with the target assigned by the ACARE SRIA FlightPath 2050 goals. The project is focused on the consideration of ConOps for ATM and air transport integration in intermodal transport network serving urban and extended urban (up to regional level) mobility, taking into account the transportation and passengers service scenarios envisaged for the next decades, according to baseline (2025), intermediate (2035) and final (2050) time horizons. In this paper, the outcomes of the first phase of the project activities, aimed to provide the initial definition (concept outline) of the proposed overall ConOps are illustrated, emphasizing the specific activities that have been carried out up to date and the related achievements. In addition, an outlook is provided in the paper on the next project activities, expected to be carried out towards the conclusion of the studies and the validation, by means of dedicated numerical simulation campaigns, of the proposed ConOps

A cross-sectional study on the impact of the COVID-19 pandemic on psychological outcomes: Multiple indicators and multiple causes modeling

Although the psychological impact of coronavirus disease 2019 (COVID-19) has been evaluated in the literature, further research is needed, particularly on post-traumatic stress disorder (PTSD) and psychological outcomes, is needed. This study aims to investigate the effect of the COVID-19 pandemic on psychological outcomes (depression, anxiety, and insomnia). A cross-sectional study using an online survey was conducted using the following instruments: Impact of Event Scale-Revised (IES-R), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder (GAD-7), and Insomnia Severity Index (ISI). Confirmatory factor analysis (CFA), structural equation model (SEM), multiple indicators and multiple causes (MIMIC) modeling, and differential item functioning (DIF) were performed to analyze the collected data. According to the results, participants with PTSD (n = 360) showed a higher level of depression, anxiety, and insomnia than those without PTSD (n = 639). Among the participants, 36.5% experienced moderate to severe symptoms of depression, and 32.6% had mild depressive symptoms. Moreover, 23.7% of participants experienced moderate to severe anxiety symptoms, and 33.1% had mild anxiety symptoms. In addition, 51.5% of participants experienced symptoms of insomnia. In conclusion, the PTSD caused by COVID-19 is significantly associated with depression, anxiety, and insomnia at the level of latent constructs and observed variables.Scopu

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations